Overview

Dataset statistics

Number of variables14
Number of observations590
Missing cells0
Missing cells (%)0.0%
Duplicate rows128
Duplicate rows (%)21.7%
Total size in memory64.7 KiB
Average record size in memory112.2 B

Variable types

Numeric13
Categorical1

Warnings

Dataset has 128 (21.7%) duplicate rows Duplicates
great has 455 (77.1%) zeros Zeros
spiritual has 465 (78.8%) zeros Zeros
mind has 451 (76.4%) zeros Zeros
shall has 446 (75.6%) zeros Zeros
things has 388 (65.8%) zeros Zeros
heart has 448 (75.9%) zeros Zeros
knowledge has 470 (79.7%) zeros Zeros
soul has 459 (77.8%) zeros Zeros
may has 464 (78.6%) zeros Zeros
life has 392 (66.4%) zeros Zeros
men has 471 (79.8%) zeros Zeros
man has 343 (58.1%) zeros Zeros
one has 340 (57.6%) zeros Zeros

Reproduction

Analysis started2021-04-30 09:58:21.288390
Analysis finished2021-04-30 09:58:47.841236
Duration26.55 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

great
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3457627119
Minimum0
Maximum7
Zeros455
Zeros (%)77.1%
Memory size4.7 KiB
2021-04-30T11:58:47.910417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7898597657
Coefficient of variation (CV)2.284398342
Kurtosis17.36315425
Mean0.3457627119
Median Absolute Deviation (MAD)0
Skewness3.493188915
Sum204
Variance0.6238784495
MonotocityNot monotonic
2021-04-30T11:58:47.996216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0455
77.1%
192
 
15.6%
229
 
4.9%
38
 
1.4%
43
 
0.5%
71
 
0.2%
61
 
0.2%
51
 
0.2%
ValueCountFrequency (%)
0455
77.1%
192
 
15.6%
229
 
4.9%
38
 
1.4%
43
 
0.5%
ValueCountFrequency (%)
71
 
0.2%
61
 
0.2%
51
 
0.2%
43
 
0.5%
38
1.4%

spiritual
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5237288136
Minimum0
Maximum15
Zeros465
Zeros (%)78.8%
Memory size4.7 KiB
2021-04-30T11:58:48.095292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.408901965
Coefficient of variation (CV)2.690136439
Kurtosis28.52126027
Mean0.5237288136
Median Absolute Deviation (MAD)0
Skewness4.488912451
Sum309
Variance1.985004748
MonotocityNot monotonic
2021-04-30T11:58:48.188042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0465
78.8%
153
 
9.0%
231
 
5.3%
316
 
2.7%
49
 
1.5%
58
 
1.4%
84
 
0.7%
151
 
0.2%
101
 
0.2%
71
 
0.2%
ValueCountFrequency (%)
0465
78.8%
153
 
9.0%
231
 
5.3%
316
 
2.7%
49
 
1.5%
ValueCountFrequency (%)
151
 
0.2%
101
 
0.2%
84
0.7%
71
 
0.2%
61
 
0.2%

mind
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5050847458
Minimum0
Maximum43
Zeros451
Zeros (%)76.4%
Memory size4.7 KiB
2021-04-30T11:58:48.323681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.023413355
Coefficient of variation (CV)4.006086844
Kurtosis331.8870089
Mean0.5050847458
Median Absolute Deviation (MAD)0
Skewness16.20146962
Sum298
Variance4.094201606
MonotocityNot monotonic
2021-04-30T11:58:48.414614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0451
76.4%
175
 
12.7%
236
 
6.1%
314
 
2.4%
48
 
1.4%
72
 
0.3%
431
 
0.2%
91
 
0.2%
61
 
0.2%
51
 
0.2%
ValueCountFrequency (%)
0451
76.4%
175
 
12.7%
236
 
6.1%
314
 
2.4%
48
 
1.4%
ValueCountFrequency (%)
431
0.2%
91
0.2%
72
0.3%
61
0.2%
51
0.2%

shall
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.979661017
Minimum0
Maximum32
Zeros446
Zeros (%)75.6%
Memory size4.7 KiB
2021-04-30T11:58:48.523906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.55
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.981077254
Coefficient of variation (CV)2.516126353
Kurtosis9.19126522
Mean1.979661017
Median Absolute Deviation (MAD)0
Skewness3.010327452
Sum1168
Variance24.81113061
MonotocityNot monotonic
2021-04-30T11:58:48.630649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0446
75.6%
135
 
5.9%
213
 
2.2%
69
 
1.5%
88
 
1.4%
98
 
1.4%
57
 
1.2%
116
 
1.0%
36
 
1.0%
106
 
1.0%
Other values (18)46
 
7.8%
ValueCountFrequency (%)
0446
75.6%
135
 
5.9%
213
 
2.2%
36
 
1.0%
45
 
0.8%
ValueCountFrequency (%)
321
0.2%
271
0.2%
262
0.3%
242
0.3%
231
0.2%

therefore
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
0
469 
1
92 
2
 
20
3
 
7
6
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters590
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0469
79.5%
192
 
15.6%
220
 
3.4%
37
 
1.2%
62
 
0.3%
2021-04-30T11:58:48.869011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-30T11:58:48.943650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0469
79.5%
192
 
15.6%
220
 
3.4%
37
 
1.2%
62
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0469
79.5%
192
 
15.6%
220
 
3.4%
37
 
1.2%
62
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number590
100.0%

Most frequent character per category

ValueCountFrequency (%)
0469
79.5%
192
 
15.6%
220
 
3.4%
37
 
1.2%
62
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common590
100.0%

Most frequent character per script

ValueCountFrequency (%)
0469
79.5%
192
 
15.6%
220
 
3.4%
37
 
1.2%
62
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII590
100.0%

Most frequent character per block

ValueCountFrequency (%)
0469
79.5%
192
 
15.6%
220
 
3.4%
37
 
1.2%
62
 
0.3%

things
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.793220339
Minimum0
Maximum12
Zeros388
Zeros (%)65.8%
Memory size4.7 KiB
2021-04-30T11:58:49.046405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.611067875
Coefficient of variation (CV)2.031047107
Kurtosis10.72393293
Mean0.793220339
Median Absolute Deviation (MAD)0
Skewness3.010490579
Sum468
Variance2.595539697
MonotocityNot monotonic
2021-04-30T11:58:49.148102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0388
65.8%
1107
 
18.1%
237
 
6.3%
316
 
2.7%
415
 
2.5%
612
 
2.0%
56
 
1.0%
94
 
0.7%
73
 
0.5%
121
 
0.2%
ValueCountFrequency (%)
0388
65.8%
1107
 
18.1%
237
 
6.3%
316
 
2.7%
415
 
2.5%
ValueCountFrequency (%)
121
 
0.2%
94
 
0.7%
81
 
0.2%
73
 
0.5%
612
2.0%

heart
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4610169492
Minimum0
Maximum6
Zeros448
Zeros (%)75.9%
Memory size4.7 KiB
2021-04-30T11:58:49.247001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.009802036
Coefficient of variation (CV)2.190379417
Kurtosis7.278534536
Mean0.4610169492
Median Absolute Deviation (MAD)0
Skewness2.652933735
Sum272
Variance1.019700153
MonotocityNot monotonic
2021-04-30T11:58:49.334738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0448
75.9%
174
 
12.5%
231
 
5.3%
321
 
3.6%
48
 
1.4%
57
 
1.2%
61
 
0.2%
ValueCountFrequency (%)
0448
75.9%
174
 
12.5%
231
 
5.3%
321
 
3.6%
48
 
1.4%
ValueCountFrequency (%)
61
 
0.2%
57
 
1.2%
48
 
1.4%
321
3.6%
231
5.3%

knowledge
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3457627119
Minimum0
Maximum6
Zeros470
Zeros (%)79.7%
Memory size4.7 KiB
2021-04-30T11:58:49.436466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8479120534
Coefficient of variation (CV)2.452294664
Kurtosis13.12787846
Mean0.3457627119
Median Absolute Deviation (MAD)0
Skewness3.315295881
Sum204
Variance0.7189548502
MonotocityNot monotonic
2021-04-30T11:58:49.550197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0470
79.7%
172
 
12.2%
229
 
4.9%
38
 
1.4%
47
 
1.2%
62
 
0.3%
52
 
0.3%
ValueCountFrequency (%)
0470
79.7%
172
 
12.2%
229
 
4.9%
38
 
1.4%
47
 
1.2%
ValueCountFrequency (%)
62
 
0.3%
52
 
0.3%
47
 
1.2%
38
 
1.4%
229
4.9%

soul
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4423728814
Minimum0
Maximum8
Zeros459
Zeros (%)77.8%
Memory size4.7 KiB
2021-04-30T11:58:49.654478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.55
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.046897493
Coefficient of variation (CV)2.366549887
Kurtosis13.79084407
Mean0.4423728814
Median Absolute Deviation (MAD)0
Skewness3.297899993
Sum261
Variance1.09599436
MonotocityNot monotonic
2021-04-30T11:58:49.749255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0459
77.8%
162
 
10.5%
239
 
6.6%
314
 
2.4%
48
 
1.4%
55
 
0.8%
82
 
0.3%
61
 
0.2%
ValueCountFrequency (%)
0459
77.8%
162
 
10.5%
239
 
6.6%
314
 
2.4%
48
 
1.4%
ValueCountFrequency (%)
82
 
0.3%
61
 
0.2%
55
 
0.8%
48
1.4%
314
2.4%

may
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3288135593
Minimum0
Maximum8
Zeros464
Zeros (%)78.6%
Memory size4.7 KiB
2021-04-30T11:58:49.852978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7970767575
Coefficient of variation (CV)2.424099417
Kurtosis21.0957728
Mean0.3288135593
Median Absolute Deviation (MAD)0
Skewness3.800972248
Sum194
Variance0.6353313574
MonotocityNot monotonic
2021-04-30T11:58:49.975646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0464
78.6%
188
 
14.9%
221
 
3.6%
39
 
1.5%
46
 
1.0%
81
 
0.2%
51
 
0.2%
ValueCountFrequency (%)
0464
78.6%
188
 
14.9%
221
 
3.6%
39
 
1.5%
46
 
1.0%
ValueCountFrequency (%)
81
 
0.2%
51
 
0.2%
46
 
1.0%
39
1.5%
221
3.6%

life
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6050847458
Minimum0
Maximum6
Zeros392
Zeros (%)66.4%
Memory size4.7 KiB
2021-04-30T11:58:50.083359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.054527587
Coefficient of variation (CV)1.742776684
Kurtosis3.939663748
Mean0.6050847458
Median Absolute Deviation (MAD)0
Skewness2.018322398
Sum357
Variance1.112028431
MonotocityNot monotonic
2021-04-30T11:58:50.177124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0392
66.4%
1107
 
18.1%
247
 
8.0%
325
 
4.2%
415
 
2.5%
53
 
0.5%
61
 
0.2%
ValueCountFrequency (%)
0392
66.4%
1107
 
18.1%
247
 
8.0%
325
 
4.2%
415
 
2.5%
ValueCountFrequency (%)
61
 
0.2%
53
 
0.5%
415
 
2.5%
325
4.2%
247
8.0%

men
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3881355932
Minimum0
Maximum7
Zeros471
Zeros (%)79.8%
Memory size4.7 KiB
2021-04-30T11:58:50.284835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.55
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9924305616
Coefficient of variation (CV)2.556917167
Kurtosis13.68518559
Mean0.3881355932
Median Absolute Deviation (MAD)0
Skewness3.459565272
Sum229
Variance0.9849184196
MonotocityNot monotonic
2021-04-30T11:58:50.383544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0471
79.8%
168
 
11.5%
221
 
3.6%
316
 
2.7%
45
 
0.8%
64
 
0.7%
54
 
0.7%
71
 
0.2%
ValueCountFrequency (%)
0471
79.8%
168
 
11.5%
221
 
3.6%
316
 
2.7%
45
 
0.8%
ValueCountFrequency (%)
71
 
0.2%
64
 
0.7%
54
 
0.7%
45
 
0.8%
316
2.7%

man
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.433898305
Minimum0
Maximum14
Zeros343
Zeros (%)58.1%
Memory size4.7 KiB
2021-04-30T11:58:50.504855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.617987289
Coefficient of variation (CV)1.825783097
Kurtosis5.906129775
Mean1.433898305
Median Absolute Deviation (MAD)0
Skewness2.441990711
Sum846
Variance6.853857443
MonotocityNot monotonic
2021-04-30T11:58:50.610574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0343
58.1%
188
 
14.9%
251
 
8.6%
425
 
4.2%
325
 
4.2%
713
 
2.2%
810
 
1.7%
59
 
1.5%
106
 
1.0%
124
 
0.7%
Other values (5)16
 
2.7%
ValueCountFrequency (%)
0343
58.1%
188
 
14.9%
251
 
8.6%
325
 
4.2%
425
 
4.2%
ValueCountFrequency (%)
141
 
0.2%
133
0.5%
124
0.7%
114
0.7%
106
1.0%

one
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8016949153
Minimum0
Maximum14
Zeros340
Zeros (%)57.6%
Memory size4.7 KiB
2021-04-30T11:58:50.727290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.394750226
Coefficient of variation (CV)1.739751867
Kurtosis19.74725646
Mean0.8016949153
Median Absolute Deviation (MAD)0
Skewness3.499281644
Sum473
Variance1.945328192
MonotocityNot monotonic
2021-04-30T11:58:50.821040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0340
57.6%
1141
23.9%
263
 
10.7%
320
 
3.4%
412
 
2.0%
66
 
1.0%
83
 
0.5%
53
 
0.5%
141
 
0.2%
91
 
0.2%
ValueCountFrequency (%)
0340
57.6%
1141
23.9%
263
 
10.7%
320
 
3.4%
412
 
2.0%
ValueCountFrequency (%)
141
 
0.2%
91
 
0.2%
83
0.5%
66
1.0%
53
0.5%

Interactions

2021-04-30T11:58:24.940153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:25.121891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:25.253037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:25.394360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:25.537007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:25.679731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:25.825313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:25.969434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:26.112052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:26.248716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:26.387345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:26.526970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:26.668592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:26.812338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:26.938997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:27.077318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:27.206675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:27.342950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:27.488090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:27.629740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:27.779310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:27.915987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.060559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.202192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.343843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.468508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.594144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.715847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.840485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:28.963185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:29.084283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:29.206598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:29.337764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-30T11:58:30.946855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:31.084171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:31.219756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:31.354885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:31.498607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:31.641227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:31.786865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:31.919510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:32.060262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:32.203876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:32.352452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:32.508036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:32.657664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:32.798791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:32.939418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:33.073202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:33.203911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:33.337373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-30T11:58:33.863767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:33.993388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:34.131048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:34.268123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-30T11:58:35.431248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:35.559810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:35.701614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:35.838248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:35.969838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:36.103480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:36.237166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:36.371806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:36.514429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:36.658012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:36.788691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:36.930311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:37.070935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:37.202890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:37.333238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:37.480026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:37.617658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:37.759292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:37.901909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:38.043558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:38.183313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:38.328924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:38.458549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:38.595272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:38.735925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:38.875061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.013719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.155095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.288351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.431602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.574221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.717836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.849483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:39.981132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:40.102809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:41.387146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:41.515800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:41.647421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:41.785068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:41.920734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.055935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.192543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.327211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.458832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.596490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.735119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.864338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:42.997952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:43.134615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:43.266495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:43.394254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:43.531888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:43.671512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:43.805125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:43.939766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:44.077426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:44.214064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:44.352689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:44.478355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:44.612993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:44.749833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:44.886605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:45.019249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:45.158449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:45.300984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:45.436048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:45.601883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:45.739485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:45.877117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.015774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.143483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.276644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.409290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.539128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.667813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.802451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:46.934100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:47.062756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-30T11:58:47.197395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-30T11:58:50.948699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-30T11:58:51.198644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-30T11:58:51.433179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-30T11:58:51.663565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-30T11:58:47.424917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-30T11:58:47.724427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

greatspiritualmindshallthereforethingsheartknowledgesoulmaylifemenmanone
000000000011002
100001303002002
200200000001001
300000104000000
400000000000000
500000000041003
600000000000002
710000000000000
800000200000000
900000000000004

Last rows

greatspiritualmindshallthereforethingsheartknowledgesoulmaylifemenmanone
58010001401200121
58110010900000111
58220053600031412
58310000401013310
58420153301004631
58500020510105140
58600041600102131
58720001300200002
58810001400000226
58920000600010011

Duplicate rows

Most frequent

greatspiritualmindshallthereforethingsheartknowledgesoulmaylifemenmanonecount
00000000000000054
10000000000000111
23001000000000009
18000010000000006
28100000000000006
2000000000000025
11000000100000005
3000000000000104
7000000000100004
16000001000000014